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The intended use of the device YSIO X.pree is to visualize anatomical structures of human beings by converting an X-ray pattern into a visible image.

The device is a digital X-ray system to generate X-ray images from the whole body including the skull, chest, abdomen, and extremities. The acquired images support medical professionals to make diagnostic and/or therapeutic decisions.

YSIO X.pree is not for mammography examinations.

The YSIO X.pree is a radiography X-ray system. It is designed as a modular system with components such as a ceiling suspension with an X-ray tube, Bucky wall stand, Bucky table, X-ray generator, portable wireless, and fixed integrated detectors that may be combined into different configurations to meet specific customer needs.

The following modifications have been made to the cleared predicate device:

• Updated generator
• Updated collimator
• Updated patient table
• Updated Bucky Wall Stand
• New X.wi-D 24 portable wireless detector
• New virtual AEC selection
• New status indicator lights

The provided 510(k) clearance letter and summary for the YSIO X.pree device (K250738) indicate that the device is substantially equivalent to a predicate device (K233543). The submission primarily focuses on hardware and minor software updates, asserting that these changes do not impact the device's fundamental safety and effectiveness.

However, the provided text does not contain the detailed information typically found in a clinical study report regarding acceptance criteria, sample sizes, ground truth establishment, or expert adjudication for an AI-enabled medical device. This submission appears to be for a conventional X-ray system with some "AI-based" features like auto-cropping and auto-collimation, which are presented as functionalities that assist the user rather than standalone diagnostic algorithms requiring extensive efficacy studies for regulatory clearance.

Based on the provided document, here's an attempt to answer your questions, highlighting where information is absent or inferred:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, or image quality scores) with corresponding reported device performance values for the AI features. The "acceptance" appears to be qualitative and based on demonstrating equivalence to the predicate device and satisfactory usability/image quality.

If we infer acceptance criteria from the "Summary of Clinical Tests" and "Conclusion as to Substantial Equivalence," the criteria seem to be:

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated as a number of cases or images. The "Customer Use Test (CUT)" was performed at two university hospitals.
• Data Provenance: The Customer Use Test (CUT) was performed at "Universitätsklinikum Augsburg" in Augsburg, Germany, and "Klinikum rechts der Isar, Technische Universität München" in Munich, Germany. The document states "clinical image quality evaluation by a US board-certified radiologist" for the new detector, implying that the images themselves might have originated from the German sites but were reviewed by a US expert. The study design appears to be prospective in the sense that the new device was evaluated in a clinical setting in use rather than historical data being analyzed.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Experts

• Number of Experts: For the overall system testing (CUT), it's not specified how many clinicians/radiologists were involved in assessing "usability," "performance," and "image quality." For the new wireless detector (X.wi-D24), it states "a US board-certified radiologist."
• Qualifications of Experts: For the new wireless detector's image quality evaluation, the expert was a "US board-certified radiologist." No specific experience level (e.g., years of experience) is provided.

4. Adjudication Method for the Test Set

No explicit adjudication method (e.g., 2+1, 3+1 consensus) is described for the clinical evaluation or image quality assessment. The review of the new detector was done by a single US board-certified radiologist, not multiple independent readers with adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and what was the effect size of how much human readers improve with AI vs. without AI assistance.

• MRMC Study: No MRMC comparative effectiveness study is described where human readers' performance with and without AI assistance was evaluated. The AI features mentioned (Auto Cropping, Auto Thorax Collimation, Auto Long-Leg/Full-Spine collimation) appear to be automatic workflow enhancements rather than diagnostic AI intended to directly influence reader diagnostic accuracy.
• Effect Size: Not applicable, as no such study was conducted or reported.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done.

The document does not describe any standalone performance metrics for the AI-based features (Auto Cropping, Auto Collimation). These features seem to be integrated into the device's operation to assist the user, rather than providing a diagnostic output that would typically be evaluated in a standalone study. The performance of these AI functions would likely be assessed as part of the overall "usability" and "performance" checks.

7. The Type of Ground Truth Used

• For the overall system and the new detector, the "ground truth" seems to be expert opinion/consensus (qualitative clinical assessment) on the system's performance, usability, and the adequacy of image quality for radiographic use. There is no mention of pathology, outcomes data, or other definitive "true" states related to findings on the images.

8. The Sample Size for the Training Set

The document does not provide any information about a training set size for the AI-based auto-cropping and auto-collimation features. This is typical for 510(k) submissions of X-ray systems where such AI features are considered ancillary workflow tools rather than primary diagnostic aids.

9. How the Ground Truth for the Training Set was Established

Since no training set information is provided, there is no information on how ground truth was established for any training data.

In summary: The 510(k) submission for the YSIO X.pree focuses on demonstrating substantial equivalence for an updated X-ray system. The "AI-based" features appear to be workflow automation tools that were assessed as part of general system usability and image quality in a "Customer Use Test" and a limited clinical image quality evaluation for the new detector. It does not contain the rigorous quantitative performance evaluation data for AI software as might be seen for a diagnostic AI algorithm that requires a detailed clinical study for clearance.

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The intended use of the device YSIO X.pree is to visualize anatomical structures of human beings by converting an X-ray pattern into a visible image.

The device is a digital X-ray system to generate X-ray images from the whole body including the skull, chest, abdomen, and extremities. The acquired images support medical professionals to make diagnostic and/or therapeutic decisions.

YSIO X.pree is not for mammography examinations.

The YSIO X.pree is a radiography X-ray system. It is designed as a modular system with components such as a ceiling suspension with an X-ray tube, Bucky wall stand, Bucky table, X-ray generator, portable wireless, and fixed integrated detectors that may be combined into different configurations to meet specific customer needs.

The following modifications have been made to the cleared predicate device:

• -New Camera Model in Collimator
• -New Auto Collimation Function: Auto Long-Leg/Full-Spine
• -Two new wireless detectors

The provided text is a 510(k) summary for the YSIO X.pree X-ray system. It describes the device, its intended use, and comparisons to predicate and reference devices. However, it does not contain the detailed clinical study information typically required to directly answer all aspects of your request regarding acceptance criteria and performance metrics for an AI/CADe device.

Specifically, the document mentions:

• A "Customer Use Test (CUT)" was performed at the "Universitätsklinikum Augsburg, Germany," focusing on "System function and performance-related clinical workflow, Image quality, Ease of use, Overall performance and stability."
• "The results of the clinical test stated that the intended use of the system was met, and the clinical need covered."
• "All images acquired with the new detectors were sufficiently acceptable for radiographic usage."

This summary indicates that new features, particularly the "Auto Collimation Function: Auto Long-Leg/Full-Spine" which is AI-based (taken from the MULTIX Impact algorithm, K213700), underwent testing. However, the FDA 510(k) summary does not include the specific acceptance criteria with reported performance against those criteria, nor detailed information about the study design (sample size, ground truth establishment, expert qualifications, etc.) for the AI-based auto collimation feature. The "Customer Use Test" appears to be a general usability and performance test for the overall system and new detectors, rather than a rigorous performance study for an AI algorithm with specific quantitative metrics.

Therefore, I cannot fully complete the table and answer all questions with the provided text. I can only extract what is present.

Here's a breakdown of what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table of explicit acceptance criteria for the AI-based auto collimation function with corresponding quantitative performance metrics (e.g., accuracy, precision for delimiting regions of interest). It only states that the overall system and new detectors' images were "sufficiently acceptable for radiographic usage" and that the "intended use of the system was met, and the clinical need covered."

2. Sample size used for the test set and the data provenance:

• Test set sample size for AI-based auto collimation: Not specified in the provided text.
• Data Provenance: The Customer Use Test (CUT) was performed at "Universitätsklinikum Augsburg, Germany." This suggests prospective data collection in a clinical setting in Germany for the general system and new detectors. It is not explicitly stated if the AI-based auto collimation performance was evaluated on this specific dataset, or if a separate dataset (and its provenance) was used for validating the AI. Given the AI algorithm was "taken over" from the MULTIX Impact (K213700) and that previous 510(k) for that device might contain more details.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not specified for any specific ground truth establishment (especially for the AI-based auto collimation). The "Customer Use Test" involved clinical evaluation, implying healthcare professionals (presumably radiologists or radiographers) were involved, but their number and specific qualifications for establishing ground truth for AI performance are not detailed.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Not specified.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No MRMC comparative effectiveness study is described for the AI-based auto collimation. The document focuses on device safety and substantial equivalence to a predicate, not enhancement of human reader performance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not explicitly detailed. The AI auto collimation feature is integrated into the workflow, implying it assists, but a standalone technical performance study for the AI component itself is not described with quantitative results. The statement that the "Multix Impact algorithm has been taken over" suggests that its performance characteristics might have been established during the clearance of the MULTIX Impact (K213700), but those details are not in this document.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not specified for the AI-based auto collimation. For the general system usability and image quality, the "Customer Use Test" implies a clinical assessment, likely representing expert (clinician) judgment.

8. The sample size for the training set:

• Not specified. The document states that the AI algorithm was "taken over" from the MULTIX Impact. This implies the training was done previously for the MULTIX Impact, but the size of that training set is not provided here.

9. How the ground truth for the training set was established:

• Not specified, for the same reasons as in point 8.

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The device is a digital X-ray system to generate X-ray images from the whole body including the skull, chest, abdomen, and extremities. The acquired images support medical professionals to make diagnostic and/or therapeutic decisions. Generic clinical benefits of radiographic examinations within the intended use are applicable for this device.

YSIO X.pree is not for mammography examinations.

The YSIO X.pree is a radiography X-ray system. It is designed as a modular system with components such as a ceiling suspension with X-ray tube, Bucky wall stand, Bucky table, X-ray generator, portable wireless and fixed integrated detectors that may be combined into different configurations to meet specific customer needs.

The provided document is a 510(k) summary for the Siemens YSIO X.pree X-ray system. It does not contain information about the acceptance criteria or a study proving the device meets specific performance criteria for an AI/CAD-related product.

The document primarily focuses on establishing substantial equivalence to a predicate device (Ysio Max) based on technological characteristics, intended use, and compliance with general safety and performance standards for X-ray systems.

Specifically, the document states:

• "Al-based Auto Cropping" is a feature described as a "New Algorithm," but the comparison table explicitly states it "does not affect safety or effectiveness." This implies that its performance was not a critical factor in the substantial equivalence determination for this 510(k). The document does not provide any performance metrics or studies related to this AI feature.
• The comparison tables highlight changes in DQE and MTF for the "MAX mini" detector, noting "small changes...does not affect safety and effectiveness." These are technical specifications of the detector, not overall system performance against clinical or perceptual criteria.

Therefore, since the document does not seem to describe an AI/CAD device that requires specific clinical performance testing against established acceptance criteria, I cannot fulfill the request for a table of acceptance criteria and associated study details from the provided text.

The information requested, such as sample size, ground truth establishment, expert adjudication, MRMC studies, and standalone performance, is typically found in submissions for AI/CAD-assisted diagnostic devices where the AI's performance is central to the safety and effectiveness claim. This 510(k) notice is for a general radiographic X-ray system, where the primary focus is on the hardware and its general imaging capabilities.

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Ysio Max is a device intended to visualize anatomical structures by converting an X-ray pattern into a visible image. Ysio Max enables radiographic and tomographic exposures of the whole body and may be used on pediatric, adult and bariatric patients. It can also be used for emergency applications.

Ysio Max is not for mammography examinations.

The Ysio Max is a stationary X-ray system for radiography. The main components are the X-ray generator, the X-ray tube with collimator supported by a ceiling rail system. The Ysio Max comes with SSXIs (Solid State X-ray Imagers) that can be mobile or fixed in the Bucky tray. An image processing and data management system (syngo FLC) completes the radiographic suite.

The purpose of this submission is the upgrade to a new software version VF10 and minor hardware changes. The modified Yisio Max will introduce the following new features:

• The operating system will be MS Windows 10
• New cybersecurity features
• Additional pediatric programs
• Implementing a "Virtual Machine" that supports hospital IT
• The SSXIs have been updated
• The EMC (Electromagnetic Compatibility was tested according to the IEC . 4th edition)
• . The image processing algorithms (Diamond view Plus) will be used for exposures without grid.

The provided text is a 510(k) premarket notification for a medical device called Ysio Max. It focuses on demonstrating substantial equivalence to a predicate device after a software upgrade (VF10) and minor hardware changes.

Based on the provided document, the device (Ysio Max with VF10 software) is a stationary X-ray system, not an AI/ML-powered device for which acceptance criteria typically involve performance metrics like sensitivity, specificity, or AUC.

Therefore, the acceptance criteria and study detailed in the document are primarily related to demonstrating that the updated device maintains the same safety and effectiveness as its predicate device and complies with relevant performance standards, rather than proving a specific diagnostic accuracy or improvement in human reader performance using AI assistance.

Here's a breakdown of the information as it applies to this specific submission, addressing your points where relevant:

1. A table of acceptance criteria and the reported device performance:

The acceptance criteria are implicitly tied to demonstrating continued substantial equivalence to the predicate device and compliance with applicable industry standards and FDA regulations. Performance is reported through comparisons to the predicate and confirmation of adherence to standards.

2. Sample sizes used for the test set and the data provenance:

• Test Set Sample Size: The document does not specify a "test set" in the context of an AI/ML algorithm being evaluated on a dataset of patient cases. Instead, the testing is described as verification and validation (V&V) testing of the software and hardware components, and performance measurements of the X-ray detectors against technical specifications. These are engineering and performance tests, not clinical studies on a patient cohort for diagnostic AI.
• Data Provenance: Not applicable in the context of diagnostic data for AI. The testing is internal to the manufacturer.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable as this is not an AI/ML diagnostic system requiring expert interpretation as ground truth. The "ground truth" for the device's performance is adherence to engineering specifications and regulatory standards.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Not applicable. This is not a study involving human reader interpretations of medical images that would require adjudication.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No, an MRMC study was not done. The document explicitly states: "For the subject of this premarket submission, Siemens did not do an evaluation of the clinical image quality as X-ray technology; geometry and SSXI changes are minor." This device is an X-ray system itself, not an AI assistant intended to improve human reader performance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not applicable. The Ysio Max is a medical imaging device (a stationary X-ray system), not a standalone diagnostic algorithm. Its performance is measured by its ability to generate images and comply with technical and safety standards.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• The "ground truth" for this submission is based on engineering specifications, compliance with standardized test methods (e.g., IEC standards for electrical safety, EMC, radiation protection), and measurable physical characteristics of the detectors (DQE, MTF). It's a technical "ground truth" rather than a clinical diagnostic one.

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device that requires a training set of data.

9. How the ground truth for the training set was established:

• Not applicable for the same reason mentioned above.

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The Ysio Max is a radiographic system used in hospitals, clinics, and medical practices. Ysio Max enables radiographic and tomographic exposures of the whole body including; skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatric patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma, critical ill) applications. Exposures may be taken with the patient sitting, standing, or in the prone position. The Ysio Max system is not meant for mammography.

The Ysio Max uses integrated or portable digital detectors for generating diagnostic images by converting x-rays into electronic signals. Ysio Max is also designed to be used with conventional film/screen or Computed Radiography (CR) cassettes.

The Ysio Max Radiography X-ray system is designed as a modular system with components such as ceiling suspension with X-ray tube, Bucky wall stand, Bucky table, X-ray generator, portable wireless and fixed integrated detectors, that may be combined into different configurations to meet specific customer needs.

The Ysio Max components may be used together with fluoroscopy tables (i.e. Luminos Agile or Luminos dRF) to facilitate radiographic examinations on such tables, when not needed for fluoroscopy.

The Ysio Max Radiography X-ray system is based on the currently available predicate Ysio.

Here's an analysis of the acceptance criteria and study information for the Siemens Ysio Max device, based on the provided text.

Based on the provided 510(k) summary for the Siemens Ysio Max, no clinical studies were performed or are described for this submission. The submission states: "Clinical testing was not applicable as Ysio Max has no new or changed Indications for Use nor any new clinical applications were introduced with the modified system."

The submission focuses on demonstrating substantial equivalence to its predicate device (Ysio) based on technological characteristics and non-clinical performance testing.

Therefore, many of the requested categories related to clinical study design and ground truth are not applicable in this specific regulatory submission.

Acceptance Criteria and Reported Device Performance

Since this submission is based on substantial equivalence and non-clinical testing, the "acceptance criteria" here refer to conformance with recognized standards and successful non-clinical verification and validation. There isn't a direct table of clinical performance metrics like sensitivity/specificity.

Detailed Study Information (Based on the provided text):

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not applicable. No clinical test set. The non-clinical tests relate to software, electrical, and mechanical functionality, rather than diagnostic performance on a patient dataset.
   • Data Provenance: Not applicable for a clinical test set. Non-clinical testing would have been performed by Siemens internally in Germany (manufacturing site) and/or the US (importer/distributor).

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. There's no clinical test set for which ground truth would need to be established by experts.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not applicable. No clinical test set.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No MRMC comparative effectiveness study was performed or described. The submission is for an X-ray system, not an AI-assisted diagnostic tool in the sense of image interpretation. While the device mentions "AIM (Artificial Intelligence Mapping) feature" for movement, this relates to system control and workflow, not diagnostic image interpretation.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • Not applicable in the context of diagnostic AI. The software testing mentioned is for system control and functionality.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Not applicable. No clinical ground truth was established for this submission. For non-clinical tests, "ground truth" would correspond to expected operational parameters and correct functionality based on engineering specifications and recognized standards.

7. The sample size for the training set:

   • Not applicable. There's no mention of a training set for an AI-based diagnostic algorithm. If the "AIM (Artificial Intelligence Mapping)" feature involved machine learning, details of its training set are not provided in this 510(k) summary.

8. How the ground truth for the training set was established:

   • Not applicable. No training set for diagnostic AI is described.

Summary of Device Modifications and Justification for No Clinical Testing:

The Ysio Max is presented as substantially equivalent to the predicate Ysio. The key changes are:

• New detector generation (Trixell) offering different sizes.
• New system control software with "Free Axis Simultaneous Travel (FAST)" and "AIM (Artificial Intelligence Mapping)" for improved workflow in positioning.
• Option to process images with Riverain ClearRead (formerly SoftView), which itself has a separate 510(k) clearance (K092363). This was an existing option for the predicate.
• Ergonomic mechanical improvements (new handgrips).

The justification for not conducting clinical testing is explicitly stated as: "Clinical testing was not applicable as Ysio Max has no new or changed Indications for Use nor any new clinical applications were introduced with the modified system." This means the device continues to perform its intended function (radiographic and tomographic exposures) as the predicate, and the modifications are considered not to affect its safety and effectiveness in a way that would necessitate new clinical evidence.

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The Ysio (New RAD -FAMILY) systems are radiographic systems used in hospitals, clinics, and medical practices. Ysio enables radiographic and tomographic exposures of the whole body including: skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatric patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma, critical ill) applications. Exposures may be taken with the patient sitting, standing, or in the prone position. The Ysio system is not meant for mammography.

The Ysio uses an integrated or portable digital detector for generating diagnostic images by converting x-rays into electronic signals. Ysio is also designed to be used with conventional film/screen or Computed Radiography (CR) cassettes.

The Ysio Radiography X-ray system is designed as a set of components such as ceiling suspension, bucky wall stand, bucky table, X-ray generator, X-ray tube, a portable wireless and a fixed detector, that may be combined into different configurations to provide specialized customer requirements.

The Ysio Radiography X-ray system is based on the currently available medical devices as listed in section 5.

The provided text is a 510(k) summary for the Siemens Ysio X-ray system. This document focuses on establishing substantial equivalence to previously cleared devices rather than presenting a study with specific acceptance criteria and performance data for a new AI/CAD device.

Therefore, the requested information regarding acceptance criteria, device performance, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set details cannot be extracted from this document, as it is not a study designed to evaluate a novel algorithm's diagnostic performance.

The document states that "all equipment is subject to final performance testing," but does not provide details of specific acceptance criteria or performance results from such testing. It primarily focuses on the device's intended use, description, and substantial equivalence to existing cleared X-ray systems.

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